scholarly journals Analysis of Structural Equation Modeling as a Measuring Tool for Educational Management Research

2019 ◽  
Vol 2 (2) ◽  
pp. 129-139
Author(s):  
Afiana Qanita ◽  
Muhammad Faris Fadhlillah ◽  
Andika Supriyana
Keyword(s):  
Structural Equation Modeling ◽  
Social Status ◽  
Structural Equation ◽  
Model Identification ◽  
Model Specification ◽  
Equation Modeling ◽  
Test Model ◽  
Literature Study ◽  
Educational Management ◽  
Reliability Factor

In management education and psychology there are certain concepts that cannot bewell defined and then various discussions arise about the true meaning of the concept.Concepts such as management intelligence, personality, attitudes, interests, ambitions, socialprejudice and social status are hypothetical constructs that are not available in operationalmethods that can directly measure them. Concepts such as intelligence, personality, attitudes,interests, ambitions, social prejudice, social status are hypothetical constructs that are notavailable operational methods that can directly measure them. This study aims to determine thebackground of the use of Structural Equation Modeling (SEM), understanding SEM, basicconcepts of SEM: constructs, manifest variables, validity, reliability, factor analysis, polycoriccorrelation, causal relationships, LISREL: Linear Structural Relationship, SEM procedures :definition of variance and covariance, model specifications, model identification, modelestimation, model formation, model compatibility test, model specification, LISREL programoutput. Symbols in SEM and SEM mathematical equations. The method used during this studytook place, namely using a literature study method which functions so that in research,researchers continue to add insight.

scholarly journals The use and misuse of structural equation modeling in management research

2014 ◽  
Vol 11 (1) ◽  
pp. 47-81 ◽  
Author(s):  
Nebojsa S. Davcik
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Conceptual Knowledge ◽  
Goodness Of Fit ◽  
Measurement Model ◽  
Model Specification ◽  
Equation Modeling ◽  
Research Practice ◽  
Management Research ◽  
Content Type

Purpose – The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria. Design/methodology/approach – The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit. Findings – The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance). Originality/value – The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

Study on the Integrated Method of SEM and DEMATEL

2014 ◽  
Vol 926-930 ◽  
pp. 3722-3727
Author(s):  
Wei Meng
Keyword(s):  
Decision Making ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fitting ◽  
Assessment Model ◽  
Model Specification ◽  
Equation Modeling ◽  
Model Assessment ◽  
Integrated Method ◽  
Model Modification

This paper compares Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory. Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory are all methods to study factors’ structure problem. Some steps of the two methods can completely replace each other and complement each other. This paper puts forward an integrated method of Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory that includes competing model specification, model fitting, model assessment, model modification and result explain.

scholarly journals FAKTOR YANG BERPENGARUH TERHADAP KOMPETENSI ENTREPRENEUR

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Helena Sidharta ◽  
Ruswiati Surya Saputra ◽  
Noor Azizi B. Ismail
Keyword(s):  
Structural Equation Modeling ◽  
Parenting Style ◽  
Big Five ◽  
Structural Equation ◽  
Important Variable ◽  
Equation Modeling ◽  
Big Five Personality ◽  
Literature Study ◽  
Factors Affecting ◽  
The Relationship

Entrepreneurial competence is an important variable that affects the success of an entrepreneur. Factors affecting entrepreneurial competence need to be researched because of strong competence needed by an entrepreneur to achieve success. Based on the literature study, education, entrepreneurial personality and parenting style are indicated to influence entrepreneurial competence. Further studies show that entrepreneurial personality and parenting style require further research because the relationship between these two variables and entrepreneurial competence needs to be understood more deeply. The result of this research is proposition development to further test the relationship between entrepreneurial personality, parenting style, and entrepreneurial competence. Furthermore, based on indicators used in previous studies, testing is suggested using structural equation modeling (SEM) because entrepreneurial personality is measured using Big Five Personality and entrepreneurial competence is measured using indicators from Man & Lau (2000) so that the indicators of both variables included in the unobserved variabl

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

scholarly journals Counterpoint

2021 ◽  
Vol 52 (SI) ◽  
pp. 114-130
Author(s):  
Xi Yu ◽  
Sam Zaza ◽  
Florian Schuberth ◽  
Jörg Henseler
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Model Identification ◽  
Theoretical Concept ◽  
Model Specification ◽  
Equation Modeling ◽  
Model Assessment ◽  
Theoretical Concepts ◽  
Is Research ◽  
Shed Light

Studying and modeling theoretical concepts is a cornerstone activity in information systems (IS) research. Researchers have been familiar with one type of theoretical concept, namely behavioral concepts, which are assumed to exist in nature and measured by a set of observable variables. In this paper, we present a second type of theoretical concept, namely forged concepts, which are designed and assumed to emerge within their environment. While behavioral concepts are classically operationalized as latent variables, forged concepts are better specified as emergent variables. Additionally, we propose composite-based structural equation modeling (SEM) as a subtype of SEM that is eminently suitable to analyze models containing emergent variables. We shed light on the composite-based SEM steps: model specification, model identification, model estimation, and model assessment. Then, we present an illustrative example from the domain of IS research to demonstrate these four steps and show how modeling with emergent variables proceeds.

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